




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCamelCase      :						str							=  logging.get_logger(__name__)

_lowerCamelCase      :						Dict							=  {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''table-transformer'''
    UpperCAmelCase__				    = ['''past_key_values''']
    UpperCAmelCase__				    = {
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
    }


    def __init__(     self :      Optional[Any]   ,					UpperCAmelCase__ :      str=True   ,					UpperCAmelCase__ :      Union[str, Any]=None   ,					UpperCAmelCase__ :      List[Any]=3   ,					UpperCAmelCase__ :      Any=100   ,					UpperCAmelCase__ :      int=6   ,					UpperCAmelCase__ :      List[str]=2_048   ,					UpperCAmelCase__ :      List[Any]=8   ,					UpperCAmelCase__ :      Dict=6   ,					UpperCAmelCase__ :      Optional[int]=2_048   ,					UpperCAmelCase__ :      str=8   ,					UpperCAmelCase__ :      str=0.0   ,					UpperCAmelCase__ :      List[str]=0.0   ,					UpperCAmelCase__ :      Dict=True   ,					UpperCAmelCase__ :      str="relu"   ,					UpperCAmelCase__ :      Tuple=256   ,					UpperCAmelCase__ :      Tuple=0.1   ,					UpperCAmelCase__ :      int=0.0   ,					UpperCAmelCase__ :      List[str]=0.0   ,					UpperCAmelCase__ :      str=0.02   ,					UpperCAmelCase__ :      List[Any]=1.0   ,					UpperCAmelCase__ :      Any=False   ,					UpperCAmelCase__ :      Optional[int]="sine"   ,					UpperCAmelCase__ :      Tuple="resnet50"   ,					UpperCAmelCase__ :      Tuple=True   ,					UpperCAmelCase__ :      str=False   ,					UpperCAmelCase__ :      Optional[int]=1   ,					UpperCAmelCase__ :      List[str]=5   ,					UpperCAmelCase__ :      Union[str, Any]=2   ,					UpperCAmelCase__ :      Tuple=1   ,					UpperCAmelCase__ :      List[str]=1   ,					UpperCAmelCase__ :      str=5   ,					UpperCAmelCase__ :      Optional[int]=2   ,					UpperCAmelCase__ :      Optional[Any]=0.1   ,					**UpperCAmelCase__ :      Any   ,					)		->List[str]:





           '''simple docstring'''

           if backbone_config is not None and use_timm_backbone:
                  raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

           if not use_timm_backbone:
                  if backbone_config is None:
                         logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
                         A__ 					=       CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
                  elif isinstance(UpperCAmelCase__   ,					UpperCAmelCase__):
                         A__ 					=       backbone_config.get('''model_type''')
                         A__ 					=       CONFIG_MAPPING[backbone_model_type]
                         A__ 					=       config_class.from_dict(UpperCAmelCase__)
                  # set timm attributes to None
                  A__					,				A__					,				A__ 					=       None, None, None

           A__ 					=       use_timm_backbone
           A__ 					=       backbone_config
           A__ 					=       num_channels
           A__ 					=       num_queries
           A__ 					=       d_model
           A__ 					=       encoder_ffn_dim
           A__ 					=       encoder_layers
           A__ 					=       encoder_attention_heads
           A__ 					=       decoder_ffn_dim
           A__ 					=       decoder_layers
           A__ 					=       decoder_attention_heads
           A__ 					=       dropout
           A__ 					=       attention_dropout
           A__ 					=       activation_dropout
           A__ 					=       activation_function
           A__ 					=       init_std
           A__ 					=       init_xavier_std
           A__ 					=       encoder_layerdrop
           A__ 					=       decoder_layerdrop
           A__ 					=       encoder_layers
           A__ 					=       auxiliary_loss
           A__ 					=       position_embedding_type
           A__ 					=       backbone
           A__ 					=       use_pretrained_backbone
           A__ 					=       dilation
           # Hungarian matcher
           A__ 					=       class_cost
           A__ 					=       bbox_cost
           A__ 					=       giou_cost
           # Loss coefficients
           A__ 					=       mask_loss_coefficient
           A__ 					=       dice_loss_coefficient
           A__ 					=       bbox_loss_coefficient
           A__ 					=       giou_loss_coefficient
           A__ 					=       eos_coefficient
           super().__init__(is_encoder_decoder=UpperCAmelCase__   ,					**UpperCAmelCase__)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->int:





           '''simple docstring'''

           return self.encoder_attention_heads





    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->int:





           '''simple docstring'''

           return self.d_model


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = version.parse('''1.11'''	)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           return OrderedDict(
               [
                   ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                   ('''pixel_mask''', {0: '''batch'''}),
               ])


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->float:





           '''simple docstring'''

           return 1e-5





    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->int:





           '''simple docstring'''

           return 12




import string

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			""""""
  for i in sequence:
    __magic_name__								=			ord(A_   )
    if 65 <= extract <= 90:
      output += chr(155 - extract   )
    elif 97 <= extract <= 122:
      output += chr(219 - extract   )
    else:
      output += i
  return output

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			string.ascii_letters
  __magic_name__								=			string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(A_   )] if c in letters else c for c in sequence   )

def a__       (			):






  '''simple docstring'''





  from timeit import timeit

  print("""Running performance benchmarks..."""   )
  __magic_name__								=			"""from string import printable ; from __main__ import atbash, atbash_slow"""
  print(f'''> atbash_slow(): {timeit('atbash_slow(printable)',	setup=A_   )} seconds'''   )
  print(f'''>      atbash(): {timeit('atbash(printable)',	setup=A_   )} seconds'''   )


if __name__ == "__main__":
    for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
        print(F'''{example} encrypted in atbash: {atbash(example)}''')
    benchmark()

